Dynamic Vision Shape from Motion

Dynamic vision is a class of problems in computer vision that calculates properties of the3D world through the dynamics of the system observing by cameras. In this thesis we introduce the issue of motion and shape estimation with the aid of a single camera and for particular objects as points, planar surfaces and polyhedrons. This problem is well known in literature as Shape from Motion. We are interested in building a framework that, starting from a sequence of images that has been recorded from a camera, achieves the geometrical and dynamics properties of the 3D scene. In the first part we inspect the motion estimation through the 2D frames recorded by the camera. In particular we present a possible recursive approach for Optical Flow estimation. The second part introduces a new class of problems in system theory that are suitable for applications of computer vision. This subject has been called perspective system theory. We consider typical issue as observability, identifiability and realization theory for this branch of problems. Results has been shown through use of algebraic and recursive estimation algorithms

Causality and synchronisation in complex systems with applications to neuroscience

This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity

Wavelet correlations to reveal multiscale coupling in geophysical systems

The interactions between climate and the environment are highly complex. Due to this complexity, process-based models are often preferred to estimate the net magnitude and directionality of interactions in the Earth System. However, these models are based on simplifications of our understanding of nature, thus are unavoidably imperfect. Conversely, observation-based data of climatic and environmental variables are becoming increasingly accessible over large scales due to the progress of space-borne sensing technologies and data-assimilation techniques. Albeit uncertain, these data enable the possibility to start unraveling complex multivariable, multiscale relationships if the appropriate statistical methods are applied. Here, we investigate the potential of the wavelet cross-correlation method as a tool for identifying multiscale interactions, feedback and regime shifts in geophysical systems. The ability of wavelet cross-correlation to resolve the fast and slow components of coupled systems is tested on synthetic data of known directionality, and then applied to observations to study one of the most critical interactions between land and atmosphere: the coupling between soil moisture and near-ground air temperature. Results show that our method is not only able to capture the dynamics of the soil moisture-temperature coupling over a wide range of temporal scales (from days to several months) and climatic regimes (from wet to dry), but also to consistently identify the magnitude and directionality of the coupling. Consequently, wavelet cross-correlations are presented as a promising tool for the study of multiscale interactions, with the potential of being extended to the analysis of causal relationships in the Earth system.Comment: Submitted to Journal of Geophysical Research - Atmospher

Causality and synchronisation in complex systems with applications to neuroscience

This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A Unified Approach for Taxonomy-based Technology Forecasting

For decision makers and researchers working in a technical domain, understanding the state of their area of interest is of the highest importance. For this reason, we consider in this chapter, a novel framework for Web-based technology forecasting using bibliometrics (i.e. the analysis of information from trends and patterns of scientific publications). The proposed framework consists of a few conceptual stages based on a data acquisition process from bibliographic online repositories: extraction of domainrelevant keywords, the generation of taxonomy of the research field of interests and the development of early growth indicators which helps to find interesting technologies in their first phase of development. To provide a concrete application domain for developing and testing our tools, we conducted a case study in the field of renewable energy and in particular one of its subfields: Waste-to-Energy (W2E). The results on this particular research domain confirm the benefit of our approach

INTELIGÊNCIA EMOCIONAL DISCENTES DE UM CURSO DE GRADUAÇÃO EM ADMINISTRAÇÃO

A inteligência emocional tem relevância no mercado de trabalho, pois quando empregada de maneira correta, tende a extrair ao máximo bons resultados de seus colaboradores, devido a isso atualmente este tema tem crescido gradativamente tornando-se uma nova capacidade para ingressar no mercado. Sendo assim o estudo tem como objetivo analisar os padrões de inteligência emocional dos discentes de um curso de administração. Para a pesquisa de campo, foi utilizada a abordagem quantitativa por meio de um instrumento de coleta de dados com escalas de Likert para mensuração dos padrões e inteligência emocional. A população alvo da pesquisa foram acadêmicos regularmente matriculados no curso de gestão em uma Instituição de Ensino superior no Brasil e a amostra foi de 190 respondentes representando erro amostral máximo de 5,64%. As dimensões com maior grau de concordância representada pela maior média é o “gerenciamento das próprias emoções” seguido da “utilização das emoções” e “gerenciamento das emoções dos outros. Por último, a dimensão com menor média foi a “percepção das emoções

Study of prompt dimuon and charm production with proton and heavy ion beams at the CERN SPS

NA6

Exoskeletons for Mobility after Spinal Cord Injury: A Personalized Embodied Approach

Endowed with inherent flexibility, wearable robotic technologies are powerful devices that are known to extend bodily functionality to assist people with spinal cord injuries (SCIs). However, rather than considering the specific psychological and other physiological needs of their users, these devices are specifically designed to compensate for motor impairment. This could partially explain why they still cannot be adopted as an everyday solution, as only a small number of patients use lower-limb exoskeletons. It remains uncertain how these devices can be appropriately embedded in mental representations of the body. From this perspective, we aimed to highlight the homeostatic role of autonomic and interoceptive signals and their possible integration in a personalized experience of exoskeleton overground walking. To ensure personalized user-centered robotic technologies, optimal robotic devices should be designed and adjusted according to the patient’s condition. We discuss how embodied approaches could emerge as a means of overcoming the hesitancy toward wearable robots

Is RRI a new R&I logic? A reflection from an integrated RRI project.

This article presents an analysis of a project in the field of assisted living technologies (ALT) for older adults where Responsible Research and Innovation (RRI) is used as an overall approach to the research and technology development work. Taking the project's three literature reviews - conducted in the fields of health science oriented towards occupational therapy, ICT research and development, and RRI - as starting points it applies perspectives from institutional logics to analyse the tension between RRI as an overall research and innovation (R&I) logic versus a disciplinary logic. This tension complicates the implementation of RRI, and we argue for giving this question more visibility. The article concludes that this project, from the funder's side and the project leader's side, was intended to be an example of research and technology development carried out within a new RRI R&I logic, but that it in large parts was conducted as a multidisciplinary project with RRI as a quasi-disciplinary logic in part in parallel with and in part in conflict with other logics in the project